Innovations in Neural Information Paradigms and Applications (eBook, PDF)

Redaktion: Bianchini, Monica; Scarselli, Franco; Maggini, Marco

• Format: PDF

Produktbeschreibung

This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include:

• Advances in neural information processing paradigms
• Self organising structures
• Unsupervised and supervised learning of graph domains
• Neural grammar networks
• Model complexity in neural network learning
• Regularization and suboptimal solutions in neural learning
• Neural networks for the classification of vectors, sequences and graphs
• Metric learning for prototype-based classification
• Ensembles of neural networks
• Fraud detection using machine learning
• Computational modelling of neural multimodal integration

This book is directed to the researchers, graduate students, professors and practitioner interested in recent advances in neural information processing paradigms and applications.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 293
• Erscheinungstermin: 13. Oktober 2009
• Englisch
• ISBN-13: 9783642040030
• Artikelnr.: 44132099

Inhaltsangabe

Advances in Neural Information Processing Paradigms.- Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels.- Unsupervised and Supervised Learning of Graph Domains.- Neural Grammar Networks.- Estimates of Model Complexity in Neural-Network Learning.- Regularization and Suboptimal Solutions in Learning from Data.- Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs.- Metric Learning for Prototype-Based Classification.- Bayesian Linear Combination of Neural Networks.- Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks.- Towards Computational Modelling of Neural Multimodal Integration Based on the Superior Colliculus Concept.